Simplified LSTM models improve sentiment analysis on Twitter debate data.
problem Performing sentiment analysis on long sequence data from Twitter debates.
method Developed six parameter-reduced LSTM models (slim LSTM) for faster training and reduced computational cost.
result Slim LSTM models outperform standard LSTM model in sentiment analysis of GOP Debate Twitter dataset.
Paper applies FloatSD8 to LSTM networks, reducing complexity and power.
problem Training and inference complexity of LSTM networks.
method Applied FloatSD8 for weights, 8-bit quantization for gradients/activations, reduced arithmetic precision.
result Successfully trained LSTM models with reduced complexity and preserved accuracy.
The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters. In this work, we present empirical comparison between the standard LSTM recurrent neural network architecture and three new parameter-r…
LSTM neural networks improve fiber nonlinearities in coherent systems.
problem Compensating fiber nonlinearities in digital coherent systems.
method Utilization of Long short-term memory (LSTM) neural networks.
result LSTM neural networks provide superior performance compared to digital back propagation, especially in multi-channel scenarios.
Improved traffic forecasting model handles missing data.
problem Short-term traffic forecasting with missing values.
method Proposed SBU-LSTM architecture with bidirectional and unidirectional LSTM.
result Superior performance in accuracy and robustness for network-wide traffic prediction.
In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To th…
MC-LSTM extends LSTM to conserve mass in neural networks.
problem Conservation laws in real-world systems.
method Extending LSTM's inductive bias to conserve mass.
result MC-LSTM sets new state-of-the-art for predicting peak flows.
LSTM neural networks improve stock price prediction for Stockholm OMX30.
problem Forecasting stock price movement in financial markets.
method Ensemble of parallel long short-term memory (LSTM) neural networks trained on binary classification of stock returns.
result The LSTM ensemble outperforms traditional portfolios in terms of average daily returns, cumulative returns, and risk-adjusted performance.
A new CNN approach for time series forecasting outperforms traditional RNNs.
problem Time series forecasting using conventional RNNs.
method Temporally folded convolutional neural networks (TFC's) for sequence forecasting.
result TFC's outperform conventional RNNs on sequential MNIST and JSB chorals datasets.
Paper uses LSTM to predict inflation, finds it performs well over long periods.
problem Predicting inflation using machine learning models.
method Applies LSTM, a recurrent neural network, to forecast inflation over time.
result LSTM model performs well at long horizons and during uncertain economic times.
Hybrid model outperforms benchmarks in financial forecasting.
problem Robust asset price forecasting in finance.
method Combining LSTM with Neural Levy Processes using Grey Wolf Optimizer and ANN calibration.
result Hybrid model outperforms base LSTM and other models.
FedMA improves federated learning for neural nets, matching and averaging model elements.
problem Federated learning's communication burden and model performance.
method Layer-wise construction of shared model by matching and averaging hidden elements.
result FedMA outperforms state-of-the-art algorithms on deep CNN and LSTM architectures.
New LSTM scheme incorporates prior knowledge and measurement uncertainties.
problem Overfitting and insufficient data for accurate time-dependent solutions.
method Sparse Bayesian training algorithm for automatic connection determination.
result Less prone to overfitting, smaller data set required for satisfying accuracy.
New method compresses LSTM networks using MPS tensor trains.
problem Challenges in maintaining performance of compressed RNNs.
method Use of MPS tensor trains for LSTM network compression.
result MPS tensor trains outperform MPOs in storage and inference time.
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic m…
Hybrid LSTM-GNN model improves stock price prediction accuracy.
problem Enhancing stock price prediction accuracy using complex data.
method Combining LSTM for time series and GNN for graph-based analysis.
result Hybrid model reduces MSE by 10.6% compared to standalone LSTM.
Study compares RNN, LSTM, and BP neural networks for forex rate prediction.
problem Improving real-time forex rate prediction accuracy.
method Analyzes RNN, LSTM, and BP neural networks' characteristics and advantages.
result Provides insights for selecting the best price-prediction model.
Generates music with coherent rhythm, chords, and melody using LSTM models.
problem Lack of direction and coherence in generated music by neural networks.
method Two-stage LSTM model: first generates harmonic and rhythmic templates, then melodies conditioned on these.
result Subjective test shows improved musical coherence and coherence compared to baselines.
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions. We focus on recurrent neural networks, state of the art models in speech recogniti…
Unified statistical framework for LSTM model selection.
problem Model selection and hyperparameter tuning in LSTM networks is heuristic and computationally expensive.
method Proposes a statistical framework extending classical model selection ideas to LSTM networks.
result Improved performance of the proposed framework demonstrated on biomedical data.
This paper solves hedging in incomplete markets using neural networks.
problem Hedging in incomplete markets with risk factor, illiquidity, and discrete transaction dates.
method Proposes a jump-diffusion model and uses RNN, LSTM, and Mogrifier-LSTM neural networks for hedging strategies.
result Mogrifier-LSTM is the fastest and most effective model for hedging.
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully u…
This study uses neural networks to predict Bitcoin prices, finding GRUs outperform LSTMs.
problem Predicting Bitcoin's volatile price movements.
method Used LSTMs and GRUs for forecasting, with L2 regularization to reduce overfitting.
result GRUs models outperform LSTMs in predicting Bitcoin's price with lower MSE.
The paper introduces a new σ-LSTM cell for volatility forecasting using stylized facts.
problem Lack of explainability and stylized knowledge in neural network volatility modeling.
method Introduces a new σ-LSTM cell with a stochastic processing layer, designed to incorporate stylized facts about volatility. result Shows good out-of-sample forecasting performance with the σ-LSTM cell. Study compares LSTM and ANN architectures for forex prediction, finding ANN more efficient.
problem Forex time series prediction efficiency and resource usage.
method Comparison of Long Short-Term Memory (LSTM) and specialized ANN architectures.
result Specialized ANN architecture performs better with fewer resources and faster execution.
Dialogue act (DA) classification has been studied for the past two decades and has several key applications such as workflow automation and conversation analytics. Researchers have used, to address this problem, various traditional machine learning models, and more recently deep neural network models such as hierarchic…
A model integrates CNN and LSTM with LLM for better stock forecasting.
problem Complex stock market prediction with volatile patterns and multifaceted data.
method Two-Level Conv-LSTM Neural Network with LLM for holistic stock advising.
result Improved prediction accuracy and contextual insights.
While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved. In this chapter, we explore how to adapt the Layer-wise Relevance Propagation (LRP) technique used for explain…
GRU models with Adam optimizer outperform other combinations in stock market forecasting.
problem Comparing optimization techniques for time series forecasting in LSTM and GRU networks.
method Examined Adam and Nesterov Accelerated Gradient (NAG) on LSTM and GRU models for stock market forecasting.
result GRU models with Adam optimizer produced the lowest RMSE and outperformed other combinations.
Paper proposes CNN-LSTM model for multi-speaker speech separation.
problem Multi-speaker source separation using deep learning.
method Parallel CNN-LSTM architecture with Bayesian hyperparameter optimization.
result Parallel CNN-LSTM model outperforms LSTM-only and CNN-only models.
Study uses IMFs and neural networks to predict economic time series, enhancing interpretability.
problem Improving prediction accuracy and interpretability of economic time series.
method Intrinsic Mode Functions (IMFs) derived from economic time series, combined with DeepSHAP for interpretability.
result The last IMFs are most influential, and high-frequency IMFs introduce noise.
A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to pred…
Paper addresses LSTM stability for thermal systems using infinity-norm.
problem Stability of LSTM networks in thermal systems.
method Derived ISS∞ condition for LSTM, developed training strategy. result ISS∞-promoted LSTM outperforms other models in thermal system case study. The use of neural networks to predict airport passenger activity choices inside the terminal is presented in this paper. Three network architectures are proposed: Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM) networks, and a combination of the two. Inputs to these models are both static (passenger an…
Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However,conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of…
SRNNs achieve LSTMs' online regression performance in less time.
problem Training LSTMs is time-consuming.
method First-order training algorithm with linear time complexity.
result SRNNs provide similar regression performance as LSTMs in shorter training time.
DP-LSTM predicts stock prices using financial news with improved accuracy and privacy.
problem Predicting stock prices with financial news articles.
method Integrates financial news articles into a sentiment-ARMA model, then uses an LSTM network with differential privacy.
result Achieves up to 65.79% improvement in MSE for S&P 500 prediction.
LSTM-MDNs improve risk forecasting during turbulent periods.
problem Forecasting Value-at-Risk (VaR) during volatile market conditions.
method Implemented Long Short-Term Memory mixture density networks (LSTM-MDNs) for VaR forecasting and compared them with established models.
result LSTM-MDNs outperformed benchmark models in turbulent periods but not in calm periods.
A shallow Bi-LSTM model forecasts Bitcoin prices using engineered features.
problem Lack of high-quality historical Bitcoin data for deep learning models.
method Proposed a shallow Bi-LSTM model with feature engineering on Bitcoin data.
result A shallow deep neural network outperforms other models in Bitcoin price forecasting.
In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To …
Fully convolutional neural networks (FCN) have been shown to achieve state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our …
Improved stock price prediction using LSTM with customized loss function and analyst calls.
problem Forecasting stock prices using LSTM neural networks.
method Customized LSTM model with improved loss function, analyst calls integration, and attention units.
result Improved performance of LSTM model over ARIMA for stock price prediction.
Paper improves neural network models for MOOC student course prediction.
problem Improving neural network-based predictive methods for MOOC student course trajectory modeling.
method Enhanced LSTM networks and Transformer architecture applied to MOOC student data.
result Improved accuracy in predicting MOOC student course trajectories.
A study on power market price forecasting by deep learning is presented. As one of the most successful deep learning frameworks, the LSTM (Long short-term memory) neural network is utilized. The hourly prices data from the New England and PJM day-ahead markets are used in this study. First, a LSTM network is formulated…
Improves LSTM performance by initializing states via manifold learning.
problem Improving LSTM performance through better initialization.
method Learning an intrinsic data manifold to initialize LSTM internal states.
result Improved LSTM performance through consistent initialization.
We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform corre…
We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible inp…
Proposes LSTM for financial market trend forecasting.
problem Challenges in financial market trend forecasting.
method Uses LSTM for financial market trend forecasting.
result Improves performance compared to traditional methods.